"""
Neural Avatar - MuseTalk/StyleAvatar integration for photorealistic lip sync
Provides cutting-edge neural rendering for the VTuber avatar
"""
import asyncio
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Any
from collections import deque
from pathlib import Path
import time
import os
from loguru import logger
try:
import torch
HAS_TORCH = True
except ImportError:
HAS_TORCH = False
try:
import cv2
HAS_CV2 = True
except ImportError:
HAS_CV2 = False
@dataclass
class NeuralAvatarConfig:
"""Configuration for neural avatar rendering"""
# Model selection
model_type: str = "musetalk" # musetalk, sadtalker, styleavatar
# Face image
face_image_path: Optional[str] = None
face_crop_margin: float = 0.2
# Output settings
output_width: int = 512
output_height: int = 512
output_fps: int = 30
# Quality settings
use_half_precision: bool = True
batch_size: int = 1
# MuseTalk specific
musetalk_version: str = "v1.5"
musetalk_bbox_shift: int = 0
# Device
device: str = "cuda" if HAS_TORCH and torch.cuda.is_available() else "cpu"
class MuseTalkAvatar:
"""
MuseTalk integration for real-time lip sync
MuseTalk provides:
- 30fps+ real-time lip sync
- High quality face modification
- Multilingual support
- Single image to talking avatar
"""
def __init__(self, config: NeuralAvatarConfig):
self.config = config
self._model = None
self._face_image = None
self._face_mask = None
self._initialized = False
# Caching for performance
self._latent_cache: Dict[str, Any] = {}
self._max_cache_size = 50
self._last_audio_hash = None
logger.info(f"MuseTalkAvatar initialized (device: {config.device})")
def _cache_latent(self, key: str, value: Any):
"""Store a latent in the bounded cache, evicting oldest if full"""
self._latent_cache[key] = value
if len(self._latent_cache) > self._max_cache_size:
# Remove oldest entry
oldest = next(iter(self._latent_cache))
del self._latent_cache[oldest]
async def initialize(self, face_image_path: Optional[str] = None) -> bool:
"""Initialize MuseTalk model and prepare face image"""
if not HAS_TORCH:
logger.error("PyTorch not available for neural avatar")
return False
if not HAS_CV2:
logger.error("OpenCV not available for neural avatar")
return False
try:
# Load face image
image_path = face_image_path or self.config.face_image_path
if not image_path or not os.path.exists(image_path):
logger.error(f"Face image not found: {image_path}")
return False
self._face_image = cv2.imread(image_path)
if self._face_image is None:
logger.error(f"Failed to load face image: {image_path}")
return False
# Resize if needed
self._face_image = cv2.resize(
self._face_image,
(self.config.output_width, self.config.output_height)
)
# Try to load MuseTalk
try:
await self._load_musetalk_model()
except Exception as e:
logger.warning(f"MuseTalk model load failed: {e}")
logger.info("Using simplified neural lip sync fallback")
self._initialized = True
logger.info("Neural avatar initialized successfully")
return True
except Exception as e:
logger.error(f"Neural avatar initialization failed: {e}")
return False
async def _load_musetalk_model(self):
"""Load MuseTalk model weights"""
# MuseTalk model loading
# Note: Full implementation requires MuseTalk repository
# https://github.com/TMElyralab/MuseTalk
try:
# Check if MuseTalk is installed
from musetalk.models.musetalk import MuseTalk as MuseTalkModel
from musetalk.utils.face_parsing import FaceParsing
from musetalk.utils.audio import Audio2Feature
# Initialize models
self._audio_processor = Audio2Feature(device=self.config.device)
self._face_parser = FaceParsing(device=self.config.device)
self._model = MuseTalkModel.from_pretrained(
"TMElyralab/MuseTalk",
variant=self.config.musetalk_version
)
self._model.to(self.config.device)
if self.config.use_half_precision:
self._model.half()
self._model.eval()
logger.info("MuseTalk model loaded successfully")
except ImportError:
logger.warning("MuseTalk not installed - using fallback")
self._model = None
async def generate_frame(self, audio_chunk: np.ndarray,
sample_rate: int = 16000) -> Optional[np.ndarray]:
"""Generate a single frame with lip sync from audio chunk"""
if not self._initialized or self._face_image is None:
return None
if self._model is not None:
return await self._generate_musetalk_frame(audio_chunk, sample_rate)
else:
return await self._generate_fallback_frame(audio_chunk, sample_rate)
async def _generate_musetalk_frame(self, audio_chunk: np.ndarray,
sample_rate: int) -> Optional[np.ndarray]:
"""Generate frame using MuseTalk"""
try:
with torch.no_grad():
# Process audio to features
audio_tensor = torch.from_numpy(audio_chunk).float()
audio_features = self._audio_processor(audio_tensor, sample_rate)
# Get face crop and mask
if self._face_mask is None:
face_tensor = torch.from_numpy(self._face_image).permute(2, 0, 1).float()
self._face_mask = self._face_parser(face_tensor.unsqueeze(0))
# Generate lip sync
face_tensor = torch.from_numpy(self._face_image).permute(2, 0, 1).float()
face_tensor = face_tensor.unsqueeze(0).to(self.config.device)
if self.config.use_half_precision:
face_tensor = face_tensor.half()
audio_features = audio_features.half()
# Forward pass
output = self._model(
face_tensor,
audio_features,
self._face_mask
)
# Convert to numpy
frame = output.squeeze(0).permute(1, 2, 0).cpu().numpy()
frame = (frame * 255).astype(np.uint8)
return frame
except Exception as e:
logger.error(f"MuseTalk generation failed: {e}")
return await self._generate_fallback_frame(audio_chunk, sample_rate)
async def _generate_fallback_frame(self, audio_chunk: np.ndarray,
sample_rate: int) -> Optional[np.ndarray]:
"""Fallback frame generation with simple mouth animation"""
if self._face_image is None:
return None
frame = self._face_image.copy()
# Calculate audio intensity
rms = np.sqrt(np.mean(audio_chunk.astype(float) ** 2))
intensity = min(rms / 5000, 1.0) if len(audio_chunk) > 0 else 0.0
# Simple mouth overlay based on intensity
if intensity > 0.1:
# Draw mouth opening (simplified)
center_x = self.config.output_width // 2
center_y = int(self.config.output_height * 0.7)
mouth_height = int(10 + intensity * 30)
cv2.ellipse(
frame,
(center_x, center_y),
(40, mouth_height),
0, 0, 360,
(50, 30, 30),
-1
)
return frame
async def generate_video_segment(self, audio: np.ndarray,
sample_rate: int = 16000,
chunk_size: int = 1600) -> List[np.ndarray]:
"""Generate multiple frames for an audio segment"""
frames = []
num_chunks = len(audio) // chunk_size
for i in range(num_chunks):
start = i * chunk_size
end = start + chunk_size
chunk = audio[start:end]
frame = await self.generate_frame(chunk, sample_rate)
if frame is not None:
frames.append(frame)
return frames
def get_latent_representation(self) -> Optional[np.ndarray]:
"""Get latent representation of face for caching"""
if self._model is None or self._face_image is None:
return None
try:
with torch.no_grad():
face_tensor = torch.from_numpy(self._face_image).permute(2, 0, 1).float()
face_tensor = face_tensor.unsqueeze(0).to(self.config.device)
# Get encoder output
latent = self._model.encode(face_tensor)
return latent.cpu().numpy()
except Exception as e:
logger.error(f"Failed to get latent: {e}")
return None
async def cleanup(self):
"""Release GPU memory"""
if self._model is not None:
del self._model
self._model = None
if HAS_TORCH:
torch.cuda.empty_cache()
self._face_image = None
self._face_mask = None
self._latent_cache.clear()
self._initialized = False
class SadTalkerAvatar:
"""
SadTalker integration for 3D-aware lip sync
SadTalker provides:
- 3DMM-based facial motion
- Audio-driven 3D motion coefficients
- Single image to video
"""
def __init__(self, config: NeuralAvatarConfig):
self.config = config
self._model = None
self._face_image = None
self._initialized = False
logger.info("SadTalkerAvatar initialized")
async def initialize(self, face_image_path: Optional[str] = None) -> bool:
"""Initialize SadTalker"""
# SadTalker implementation
# https://github.com/OpenTalker/SadTalker
try:
from sadtalker.src.facerender.animate import AnimateFromCoeff
from sadtalker.src.generate_batch import get_data
from sadtalker.src.generate_facerender_batch import get_facerender_data
# Load models...
logger.info("SadTalker loaded")
self._initialized = True
return True
except ImportError:
logger.warning("SadTalker not installed")
return False
async def generate_frame(self, audio_chunk: np.ndarray,
sample_rate: int = 16000) -> Optional[np.ndarray]:
"""Generate frame using SadTalker"""
# SadTalker is typically used for batch video generation
# Real-time use requires specific optimization
return None
class NeuralAvatarManager:
"""
Manager for neural avatar backends
Handles:
- Backend selection and fallback
- Performance optimization
- Frame caching
"""
def __init__(self, config: NeuralAvatarConfig):
self.config = config
self._active_backend: Optional[Any] = None
self._backend_type: str = ""
# Frame buffer for smooth playback
self._frame_buffer: deque = deque(maxlen=10)
self._buffer_size = 10
async def initialize(self, face_image_path: str) -> bool:
"""Initialize the best available neural avatar backend"""
# Try backends in order of preference
backends = [
("musetalk", MuseTalkAvatar),
("sadtalker", SadTalkerAvatar),
]
for backend_name, backend_class in backends:
if self.config.model_type and self.config.model_type != backend_name:
continue
try:
backend = backend_class(self.config)
if await backend.initialize(face_image_path):
self._active_backend = backend
self._backend_type = backend_name
logger.info(f"Using neural avatar backend: {backend_name}")
return True
except Exception as e:
logger.warning(f"Backend {backend_name} failed: {e}")
logger.error("No neural avatar backend available")
return False
async def generate_frame(self, audio_chunk: np.ndarray,
sample_rate: int = 16000) -> Optional[np.ndarray]:
"""Generate a frame using the active backend"""
if self._active_backend is None:
return None
frame = await self._active_backend.generate_frame(audio_chunk, sample_rate)
# Add to buffer (deque handles maxlen automatically)
if frame is not None:
self._frame_buffer.append(frame)
return frame
def get_buffered_frame(self, index: int = -1) -> Optional[np.ndarray]:
"""Get a frame from the buffer"""
if not self._frame_buffer:
return None
return self._frame_buffer[index]
@property
def backend_type(self) -> str:
return self._backend_type
@property
def is_initialized(self) -> bool:
return self._active_backend is not None
async def cleanup(self):
"""Clean up the active backend"""
if self._active_backend and hasattr(self._active_backend, 'cleanup'):
await self._active_backend.cleanup()
self._active_backend = None
self._frame_buffer.clear()
